The present invention relates generally to the electrical, electronic and computer arts, and, more particularly, to techniques which utilize crowdsourced reports of environmental conditions to predict and/or prevent disease outbreaks.
Compartmental models are used in epidemiology to evaluate and predict disease outbreak. These models include SEIR models in which a human population is divided into four groups: Susceptible, Exposed, Infectious, or Recovered. An exemplary SEIR model is described in Syafruddin & Noorani, “SEIR Model for Transmission of Dengue Fever in Selangor Malaysia,” Int. J. Mod. Phys. Conf. Ser., v. 9, p. 380-389, 2012, which is incorporated by reference herein.
The onset of some diseases may be associated with environmental factors. This is especially true for diseases that depend on transmission vectors such as insects or rodents. For example, in São Paulo, Brazil in early 2015, a serious drought caused people to store pluvial water, thus favoring the proliferation of mosquitos that transmit dengue fever. As a result, there was an increase of 57% on the number of reported cases of dengue fever in the region. Drought season in many regions of the world similarly stimulate citizens to store water at home, leading to potential outbreaks of mosquito-borne diseases such as dengue fever.
However, models of disease outbreak, such as SEIR, are based on information regarding the number of infected people and their location. Thus, while these models are able to predict spread of a disease among a population with reasonable accuracy, they require that the onset of the disease has already happened. Thus, disease outbreak models are useful to infer how an already existing disease will propagate (e.g., spread geographically) over time but are unable to predict the initial onset of disease.